Systems and Methods of Classifying Content Items

ABSTRACT

A method of classifying content items utilizes user-generated playlists. A content item is included in respective playlists from a plurality of respective distinct users. The method receives respective user-generated information corresponding to the content item from each of the respective distinct users. For some users, the respective user-generated information is the respective playlist title. For other users, the user-generated information is the text of social network posting that identifies a respective playlist. In each case, the respective user-generated information specifies a first content item attribute that characterizes the content item. Accordingly, the method assigns the first content item attribute to the content item. Subsequently, a request is received from a first user for a content item having the first content item attribute. In response, the method selects the content item according to the first content item attribute and delivers the first content item to the first user.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/657,637, filed Jun. 8, 2012, entitled “Playlist Generation andAnalysis,” which is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosed implementations relate generally to classifying contentitems, and more specifically to classifying content items based onuser-generated information.

BACKGROUND

Historically, there have been two main ways to receive audio tracks. Ifa user purchases a physical medium that stores the audio tracks, thenthe user has complete control over what tracks to plan and when to planthem. However, a physical medium (such as a CD) has a fixed set of audiotracks, such as a specific “album” from a single artist. With more work,a user can “burn” additional physical media that have customizedsequences of audio tracks. However, even with that work, the list isstill fixed.

An alternative is to listen to audio tracks on a radio station. A radiostation has a very large selection of audio tracks and can play thosetracks in an endless variety of sequences. In addition, different radiostations can focus on different genres, enabling users to select thespecific type of music desired (which can vary from day to day or fromhour to hour). However, radio stations have a different set of problems.One problem is the abundance of commercials and other interruptions. Asecond problem is that the selected audio tracks may not be of interestto the listener. In fact, a user may strongly dislike some of the audiotracks that are played. A user can address these problems to some extentby switching the station or channel. However, the need to switch amongmultiple stations or channels may indicate that there is no station orchannel that is a good match for a specific user's interests.

Some companies have addressed these problems by providing streamingcontent over the Internet. In some instances, a user searches fordesired content items (e.g., audio tracks), and the desired contentitems are subsequently streamed to the user over a computer. Somewebsites provide Internet radio stations, which can be designated for asingle individual or group of individuals. The Internet radio stationsstream an endless sequence of content items, commonly withoutcommercials. In addition, if a user does not want the current contentitem, the user can execute a “skip-forward,” which prompts the Internetradio station to select and stream a new content item.

Despite the appeal of an Internet radio station as described, there arestill problems. One problem is how to select content items that bestrepresent what a user wants. This is particularly difficult when theInternet radio station has little information about a user'spreferences. Furthermore, some users are reluctant to spend their timegiving extensive information about their preferences.

In addition, many users like to listen to a radio station with aspecific genre. Historically, a DJ or other individual would selectcontent items corresponding to an identified “genre”. However, differentindividuals may have different opinions, and some of those opinions maynot correspond to what people expect. Also, even if there is commonknowledge about the classification of some content items, it may not bepossible to identify that common knowledge. As with person preferences,users are typically reluctant to spend their time providing explicitfeedback.

SUMMARY

Some implementations of the present invention address these and otherproblems. Some implementation offer a streaming music service based onsearch, play, and playlists. For instance, a user can type the name ofan artist or track and search for it to find it. The user can then clickthe found track to play it. The user can repeat this process, findingand playing new tracks they recall.

Some implementations provide for the creation of playlists. Instead ofplaying an individual track right now, a user can include it in aplaylist (e.g., by dragging it into a playlist in a user interface).This allows users to organize tracks, and play them later. Usersfrequently create multiple playlists, and can give each playlist a textname. Users create playlists to group together music that holds somecommon meaning to them.

Some implementations offer an online radio feature. This radio featureplays an endless sequence of songs. The user does not know which songwill play next. If the user doesn't like the song currently playing, a“Skip” of “Skip Forward” button moves to the next song immediately. Tocreate a new radio station, a user first identifies a “seed.” This seedcan be one or more individual tracks, one or more artists, one or morealbums, one or more playlists, a music genre, or combinations of these.A software system “programs” the radio station, choosing which tracks toplay dynamically. In some implementations, an Internet radio station isassociated with a single user or user ID. In some implementations,Internet radio stations can be shared with other users. In someimplementations, the selection criteria for an Internet radio stationare based on input from two or more users.

The techniques described above with respect to audio tracks can also beapplied more generally to other content items, such as video,animations, and even some online games.

The success of an Internet radio station can be measured based on anumber of quantitative factors, such as:

-   -   How often a user returns to the radio station;    -   How long a user listens to the radio station;    -   How many tracks a user listens versus the number of tracks that        are skipped; and    -   How many tracks a user “likes” or “dislikes” (e.g., by clicking        on a button, hyperlink, icon, etc.)

Some of the disclosed implementations analyze the data from a “searchand play” music service (e.g., where users select individual tracks,albums, or artists to listen to), as well as data relating to usage ofthe radio stations in order to program radio stations. One goal of theseideas is to reduce the number of times the user skips tracks, thusindicating that the selection algorithm for a particular radio stationis selecting tracks that the user wants to hear.

Some implementations use one or more of the following techniques:

-   -   Large scale similarity search: using LSH (Locality sensitive        hashing) to do similarity search, combined with matrix        factorization;    -   Using skip data, track preference data, time-on-radio data,        and/or repeat listening data, to enhance the radio quality;    -   Large scale iterative graph algorithms using Bloom filters to        make track selection and optimization faster; and    -   Using user-generated data (e.g., playlist names, social network        postings, etc.) to build genre stations.

Some of the disclosed implementations use large scale collaborativefiltering. Some implementations apply these algorithms to Internet radiostations. In particular, with millions of available content items, itwould be very expensive (in time and resources) to compare each of thecontent items to all of the other content items. One alternative usesmatrix factorization, or singular value decomposition (SVD). The idea isto create a usage matrix whose rows represent users and whose columnsrepresent content items. In some implementations, each entry representsthe number of times that a specific user selected a specific contentitem. It would be useful to express each entry in this matrix as aproduct of a user vector U and an item vector I. Although this cannot bedone exactly, user and item vectors can be chosen so that the vectorproducts approximate the entries in the usage matrix.

Because the usage matrix is sparse, it is fairly easy to iterativelycompute user and item vectors. For example, some implementations useabout 20 iterations, which can occur in about 24 hours when distributedacross many computers operating in parallel. Finding the user and itemvectors factors the usage matrix into a product, which is a convenientrepresentation. In some implementations, the user and item vectorscontain around 40 elements, so multiplying vectors together is quick.

In some implementations, the user and item vectors are viewed as pointsin hyperspace (e.g., with 40 dimensions). Using this representation, theproximity between two item vectors is just the inner product (or dotproduct) of two vectors. Thus, the similarity between two content itemshas been reduced to a straightforward calculation.

Unfortunately, with roughly 5 million audio tracks, there are about 25trillion possible products. Some implementations address this problem by“cutting” the 40 dimensional vector space of items with randomhyperplanes, creating a number of faceted regions. Additionalhyperplanes are added until there are few enough points in each regionso that it is possible to compare all the item vectors in each regionwith all the other item vectors in that region. Some implementations addhyperplanes until there are only a few hundred item vectors in eachregion. Depending on computing resources, the desired number of itemvectors in each faceted region may be more or less than a few hundred.

In the vector space of item vectors, the vertices are the item vectorsthemselves, representing content items like audio tracks, and the edgesrepresent relationships. Some pairs of content items are quite close inthis vector space. Some implementations “fine tune” the calculation ofproximity by assigning weights to each of the edges. For example, oneedge may have a weight of 0.9, whereas another edge has a weight of 0.2.Selecting the weights is another optimization problem. This can becompleted in about 30 iterations, taking a couple of days whendistributed across many computers operating in parallel.

The objective is to assign weights so that a user is presented withcontent items that the user likes. However, there is a sliding scale ofwhether to repeat already known items versus introducing new items. Atone end of the scale, an Internet radio station could be programmedconservatively, playing only songs that a user has already identifiedpositively and songs that are very likely to be similar to what'salready played (and identified positively). At the opposite end of thescale, an Internet radio station can play a greater variety of contentitems, introducing the listener to related but new tracks. The positionon the scale can also depend on an individual user, and thus someimplementations track how interested users are in being introduced tomore varied new music.

When assigning weights to the edges, some implementations apply Bloomfilters. The Bloom filters allow discarding many negatives whileallowing some false positives. For example, if user A has never listenedto song B, then there is no need to record that fact. Empirically,applying Bloom filters can reduce the amount of overhead by 99%.

There are a number of algorithms, or models, and several post processingsteps. Each model can independently answer the question, “How likely isit that user A will play track B next, based on the tracks the user haspreviously listened to?” For example, suppose there are 30 differentmodels, and they all produce slightly different answers. Someimplementations combine the results of the modules, which reduces noise.One of the models is a simple classifier that just favors whatever ispopular. This model ignores the user, and answers based solely on thepopularity of the track. In some implementations, the popularity modelis limited to popularity within a demographic group or within a groupthat have shown interest in the same genre(s). For example, even if acertain classic rock song is very popular, a user who is focused onhip-hop would likely have no interest in that song.

Some implementations provide genre radio stations. For example, insteadof starting a radio station based on a given artist or track, there areradio stations based more broadly around a genre of music, like rock orpop. Some implementations build genre radio stations using userplaylists. Starting with a desired genre (e.g., hip hop) the systemfinds a playlist with the genre name in the playlist title. A user mighthave named a playlist “Mark's Hip Hop Favorites” or “Really goodElectronica”. In some implementations, there may be a million hip hopplaylists. There is some “noise” in the data, but by aggregating thelists of many users, consistent patterns are detected.

In addition to identifying individual content items that are similar,some embodiments identify similarity of artists. For example, if audiotracks by artist A are found similar to the audio tracks of artist B,then it can be inferred that artist A is similar to artist B. Therefore,when a user identifies interest in a specific artist, someimplementations recommend other artists of interest. Someimplementations compute an artist vector V based on the item vectors Icorresponding to the artist (e.g., an average or weighted average). Whenartists are also represented as vectors, it is possible to make artistto track recommendations as well. For example, given a desired artist,some implementations generate a list of tracks that are similar to theartist (e.g., the top 250 similar tracks). The process of identifyingthe similar tracks can be distributed across computers, and thusperformed in a reasonable amount of time, even though there are millionsof available items.

According to some implementations, a method of selecting content itemsis performed by an electronic device having one or more processors andmemory. The memory stores one or more programs for execution by the oneor more processors. The method includes providing a first content itemto a first user. The first content item is selected from a plurality ofavailable content items, such as audio tracks or videos. The first userprovides feedback relating to the first content item, and the feedbackis used to adjust content item selection criteria for a second userdistinct from the first user. In some implementations, the feedbackprovided by the first user can be a skip-forward input (i.e., the userchooses to skip to the next content item), an indication of a positivepreference for the content item, or an indication of a negativepreference for the content item. The method includes receiving a requestfor a content item from the second user and selecting a content itemfrom the plurality of available content items for the second useraccording to the adjusted content item selection criteria. The selectedcontent item is then provided to the second user.

Some implementations provide the first content item to a plurality offirst users distinct from the second user, and utilize feedback from atleast a plurality of those first users to adjust the content itemselection criteria for the second user. Regardless of whether thefeedback is from a single first user or a plurality of first users, theadjustment of the selection criteria for the second user is based onsome correlation (or inverse correlation) between the first user(s) andthe second user. For example, if the second user has shown some interestin a specific artist, and the first user has shown an affinity for thesame specific artist, then feedback from the first user could berelevant to the second user.

Some implementations expand the described process to include a sequenceof content items, using feedback from the first user (or users)regarding the entire sequence. For example, metrics can measure thenumber of times in the sequence that the first user skipped forward, theamount of time the first user spent listening (or watching) the sequenceof content items, the number of times the first user provided positiveand/or negative feedback about content items in the sequence, whichspecific content items the user provided feedback on, or the number oftimes that the first user returned to the stream (e.g., returned to thesame Internet radio station).

According to some implementations, a method of classifying content itemsutilizes user-generated playlists. A content item is included inrespective playlists from a plurality of respective distinct users. Themethod receives respective user-generated information corresponding tothe content item from each of the respective distinct users. For someusers, the respective user-generated information is the respectiveplaylist title. For other users, the user-generated information is thetext of a social network posting that identifies a respective playlist.In each case, the respective user-generated information specifies afirst content item attribute that characterizes the content item.Accordingly, the method assigns the first content item attribute to thecontent item. Subsequently, a request is received from a first user fora content item having the first content item attribute. In response, themethod selects the content item according to the first content itemattribute and delivers the first content item to the first user.

A method of selecting content items is provided, in accordance with someimplementations. The method may be performed at an electronic devicehaving one or more processors and memory storing one or more programsfor execution by the one or more processors. (E.g., content server 106and/or client device 102.) The method includes providing a first contentitem to a first user. In some implementations, the first content item isan audio track (e.g., music) or a video. In some implementations, thefirst content item is one of a plurality of content items selected fordelivery to plurality of users, for example, as part of an internetradio station, streaming playlist, etc. In some implementations, thecontent items of the plurality of content items are selected so as to besimilar to a “seed,” such as a song, album, artist, or genre. In someimplementations, the content items of the plurality of content items areselected based on a determination that they are likely to be enjoyed bya particular user, or a particular type of user.

The method further includes receiving an input relating to the firstcontent item from the first user. In some implementations, the input isa skip forward input. In some implementations, the input relating to thefirst content item indicates a negative preference for the first contentitem. In some implementations, the input relating to the first contentitem indicates a positive preference for the first content item. In someimplementations, the absence of a skip forward input is also consideredan “input,” which is an implicit recognition that the user at leasttolerates the content item.

The method further includes adjusting content item selection criteriafor the first user and a second user separate from the first user basedat least in part on the input. In some implementations, this entailsadjusting the selection criteria not only for the user that provided theinput (e.g., so that user doesn't hear the “disliked” track again), butalso to customize selection algorithms for other users, such as a globalselection algorithm for a particular radio station. Accordingly, oneuser's actions with respect to a content item can be used as feedbackinto the overall selection criteria, as well as the process of tuningthe selection criteria. Some implementations use feedback from one userto modify selection criteria for all radio stations, selecting contentitems to present to users in a non-radio context, suggesting tracks fora user's playlist, and so on.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes providing a first content item to a first plurality of users;receiving an input relating to the first content item from the firstplurality of users; and adjusting content item selection criteria forthe first plurality of users and a second plurality of users separatefrom the first plurality of users based at least in part on the input.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes providing a first content item to a first user, wherein thefirst content item is one of a plurality of content items selected fordelivery to the first user, and wherein the plurality of content itemsare selected in accordance with first selection criteria; receiving aninput from the first user relating to the first content item; andadjusting the first selection criteria and second selection criteriabased at least in part on the input, wherein a second plurality ofcontent items are selected for delivery to a second user in accordancewith the second selection criteria.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes providing a sequence of content items to a first user (e.g.,streaming an internet radio station); determining an amount of time thatthe sequence of content items is being provided to the first user; andadjusting selection criteria for the sequence of content items based atleast in part on the amount of time that the sequence of content itemswas provided to the first user. In some implementations, the methodfurther includes adjusting second selection criteria for a secondsequence of content items based at least in part on the amount of timethat the sequence of content items was provided to the first user.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes providing a first sequence of content items to a first user,the first sequence of content items selected in accordance with firstselection criteria. In some implementations, the first sequence is aninternet radio station. In some implementations, the content items areselected so as to relate to a common theme (e.g., artist, track, album,genre, etc.). The method further includes providing a second sequence ofcontent items to a second user, the second sequence of content itemsselected in accordance with second selection criteria. The methodfurther includes, for each of the first user and the second user,identifying one or more metrics selected from the group consisting of: askip forward input; an input indicating a negative preference to arespective content item; an input indicating a positive preference to arespective content item; an amount of time that the respective sequenceof content items was provided to the user; and a number of times that arespective user initiates the respective sequence of content items. Insome implementations, the method includes using 1, 2, 3, 4, or all ofthese metrics. The method further includes determining a first score forthe first sequence and a second score for the second sequence, the firstand the second scores based at least in part on the identified one ormore metrics.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes providing a first content item to a user; receivinguser-generated information associated with the first content item, theuser-generated information having been associated with the first contentitem by the user; and assigning an attribute to the first content itembased on the user-generated information. In some implementations, theuser-generated information is from a text of a social network postingassociated with the first content item. For example, a user may post toa social network a link to a track, and comment saying “this is the bestnew hip-hop track!” The words “hip-hop” can be identified in thiscomment and associated (at the service provider) with the track. Asanother example, a user could post a link to a playlist on a socialnetwork, identifying the playlist as “music for a rainy day.” Thus,users' interactions with tracks, artists, albums, etc., can help theservice provider to further classify tracks, measure popularity,identify themes or trends, and tune selection algorithms and criteriafor internet radio stations.

Another method is provided for selecting content items in accordancewith some implementations. The method is performed at an electronicdevice having one or more processors and memory storing one or moreprograms for execution by the one or more processors. The methodincludes identifying a name associated with a user-generated playlist,the playlist comprising a plurality of content items; determining anattribute in the name, the attribute describing an aspect of theplurality of content items; assigning the attribute to at least one ofthe content items in the plurality of content items; and including theat least one content item in a sequence of content items for delivery toa user, wherein the sequence of content items is characterized at leastpartially by the attribute.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the context in which some implementations of thepresent invention operate.

FIG. 2 is a block diagram illustrating a client device in accordancewith some implementations.

FIG. 3 is a block diagram illustrating a content server in accordancewith some implementations.

FIG. 4 illustrate a process of adjusting content item selection criteriafor one user based on feedback from other users in accordance with someimplementations.

FIG. 5 illustrates processes of associating certain attributes withspecific content items based on user playlists in accordance with someimplementations.

FIGS. 6A and 6B are a flowchart of a method for selecting content itemsin accordance with some implementations.

FIGS. 7A and 7B are a flowchart of a method of classifying content itemsin accordance with some implementations.

Like reference numerals refer to corresponding parts throughout thedrawings.

DESCRIPTION OF IMPLEMENTATIONS

FIG. 1 illustrates the context in which implementations of the presentinvention operate. A plurality of users 112 access their client devices102 to run an application 110, which accesses content items provided bythe service provider 116. In some implementations, the application 110runs within a web browser 224. The application 110 communicates with theservice provider 116 over a communication network 108, which may includethe Internet, other wide areas networks, one or more local networks,metropolitan networks, or combinations of these. In someimplementations, data from online social media 114 is integrated withthe disclosed systems and methods. The service provider 116 works withthe application 110 to provide users with content items, such as audiotracks or videos. The service provider typically has one or more webservers 104, which receive requests from client devices 102, and providecontent items, web pages, or other resources in response to thoserequests. The service provider also includes one or more content servers106, which perform the methods described herein, including selectingappropriate content items for users, classifying content items, managinguser playlists, and integrating with online social media. The data usedby the content servers 106 is typically stored in a database 118,including content items 324 and associated metadata, as described belowwith respect to FIG. 3. In some implementations, the database 118 isstored at one or more of the content servers 106. In someimplementations, the database is a relational SQL database. In otherimplementations, the data is stored as files in a file system or othernon-relational database management system

The client device 102 includes an application 110, such as a mediaplayer that is capable of receiving and displaying/playing back audio,video, images, and the like. The client device 102 is any device orsystem that is capable of storing and presenting content items to auser. For example, the client device 102 can be a laptop computer, adesktop computer, tablet computer, mobile phone, television, etc.Moreover, the client device 102 can be part of, or used in conjunctionwith, another electronic device, such as a set-top-box, a television, adigital photo frame, a projector, a smart refrigerator, or a “smart”table.

In some implementations, the client device 102, or an application 110running on the client device 102, requests web pages or other contentfrom the web server 104. The web server 104, in turn, provides therequested content to the client device 102.

The content items 324 stored in the database 118 include audio tracks,images, videos, etc., which are sent to client devices 102 for access byusers 112. For example, in implementations where the application 110 isa media player, the application 110 may request media content items, andthe service provider 116 sends the requested media content items to theclient device 102.

FIG. 2 is a block diagram illustrating a client device 102 according tosome implementations. The client device 102 typically includes one ormore processing units (CPUs, sometimes called processors) 204 forexecuting programs (e.g., programs stored in memory 214), one or morenetwork or other communications interfaces 212, user interfacecomponents 206, memory 214, and one or more communication buses 202 forinterconnecting these components. The communication buses 202 mayinclude circuitry (sometimes called a chipset) that interconnects andcontrols communications between system components. In someimplementations, the user interface 206 includes a display 208 and inputdevice(s) 210 (e.g., keyboard, mouse, touchscreen, keypads, etc.). Insome implementations, the client device 102 is any device or system thatis capable of storing and presenting content items to a user. In someimplementations, the client device 102 is a mobile device, including,but not limited to, a mobile telephone, audio player, laptop computer,handheld or tablet computer, portable digital assistant, or the like. Insome implementations, the client device 102 is any a desktop (i.e.,stationary) computer. In some implementations, the client device is, oris incorporated into, a set-top-box, a television, a digital photoframe, a projector, a smart refrigerator, a “smart” table, or a mediaplayer accessory.

Memory 214 includes high-speed random access memory, such as DRAM, SRAM,DDR RAM or other random access solid state memory devices; and typicallyincludes non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 214 optionally includesone or more storage devices remotely located from the CPU(s) 204. Memory214, or alternately the non-volatile memory devices(s) within memory214, comprises a non-transitory computer readable storage medium. Insome implementations, memory 214 or the computer readable storage mediumof memory 214 stores the following programs, modules, and datastructures, or a subset thereof:

-   -   an operating system 216, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a communications module 218, which connects the client device        102 to other computers (e.g., the web server 104, the content        server 106, etc.) via the one or more communication interfaces        212 (wired or wireless) and one or more communication networks        108, such as the Internet, other wide area networks, local area        networks, metropolitan area networks, and so on;    -   a user interface module 220, which receives commands from the        user via the input device(s) 210 and generates user interface        objects in the display device 208;    -   an application 110 (e.g., a media player, a game, etc.), which        provides one or more computer-based functions to a user; and    -   a web browser 224, which allows a user to access web pages and        other resources over the web. In some implementations, the        application 110 runs within the web browser 224.

The application 110 is any program or software that provides one or morecomputer-based functions to a user. In some implementations, theapplication is a media player. In some implementations, the applicationis a computer game. The application 110 may communicate with the webserver 104, the content server 106, as well as other computers, servers,and systems.

In some implementations, the programs or modules identified abovecorrespond to sets of instructions for performing a function or methoddescribed above. The sets of instructions can be executed by one or moreprocessors (e.g., the CPUs 204). The above identified modules orprograms (i.e., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these programs or modules may be combined or otherwisere-arranged in various implementations. In some implementations, memory214 stores a subset of the modules and data structures identified above.Furthermore, memory 214 may store additional modules and data structuresnot described above.

FIG. 3 is a block diagram illustrating a content server 106 according tosome implementations. The content server 106 typically includes one ormore processing units (CPUs, sometimes called processors) 304 forexecuting programs (e.g., programs stored in memory 314), one or morenetwork or other communications interfaces 312, an optional userinterface 306, memory 314, and one or more communication buses 302 forinterconnecting these components. The communication buses 302 mayinclude circuitry (sometimes called a chipset) that interconnects andcontrols communications between system components. In someimplementations, the user interface 306 includes a display 308 and inputdevice(s) 310 (e.g., keyboard, mouse, touchscreen, keypads, etc.).

Memory 314 includes high-speed random access memory, such as DRAM, SRAM,DDR RAM or other random access solid state memory devices; and typicallyincludes non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 314 optionally includesone or more storage devices remotely located from the CPU(s) 304. Memory314, or alternately the non-volatile memory devices(s) within memory314, comprises a non-transitory computer readable storage medium. Insome implementations, memory 314 or the computer readable storage mediumof memory 314 stores the following programs, modules, and datastructures, or a subset thereof:

-   -   an operating system 316, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a communications module 318, which connects the content server        106 to other computers (e.g., the client device 102, the web        server 104, etc.) via the one or more communication interfaces        312 (wired or wireless) and one or more communication networks        108, such as the Internet, other wide area networks, local area        networks, metropolitan area networks, and so on;    -   an optional user interface module 320, which receives commands        via the input device(s) 310 and generates user interface objects        in the display device 308;    -   a content item selection module 322, which selects content items        324 for individual user and/or for Internet radio stations based        on one or more criteria;    -   a database 118, which stores content items 324 and other data        used by the content item selection module 322 and other modules        running on the content server 106. Each content item 324        includes both the playable content 326 (e.g., the actual audio        track or video), as well as metadata about the content item 324.        The metadata includes the title 328 of the content item 324, the        name(s) 330 of the artists or group (e.g., singer, band, actor,        movie producer), and other metadata 332. The database also        includes a list or table of content item attributes 334, such as        genres (e.g., “hip-hop,” “jazz,” “rock”) or themes (e.g.,        “Christmas” or “Hanukkah”). The database 118 also includes a        list of users 336, which are typically registered users. This        allows the content server to track the likes and dislikes of the        users, and thus present users with content items 324 that better        match a user's likes. In some implementations, the database        stores playlists 338 for each user, which are lists of content        items 324. A playlist may be completed constructed by the user,        or partially constructed by a user and filled in by the content        item selection module 322 (e.g., by identifying items similar to        or correlated with content items already in the playlist). An        individual user may have zero or more playlists. Some        implementations store user preferences 340 provided by each        user. When provided, user preferences may enable the content        item selection module 322 to provide better content item        selections. The database also stored item selection criteria        342. In some implementations, the criteria are stored for each        individual user separately. Some implementations enable multiple        sets of selection criteria for an individual user (e.g., for a        user who likes to listen to both jazz and classical music, but        at different times). Some implementations support group        selection criteria, which can be used independently or in        conjunction with personal item selection criteria; and    -   a social media monitor 344, which can review social media        postings to identify information about media content items. This        is illustrated in more detail below with respect to FIG. 5.

In some implementations, content items 324 are audio tracks, videos,images, interactive games, three-dimensional environments, oranimations.

In some implementations, the programs or modules identified abovecorrespond to sets instructions for performing a function or methoddescribed above, including those described above. The sets ofinstructions can be executed by one or more processors (e.g., the CPUs304). The above identified modules or programs (i.e., sets ofinstructions) need not be implemented as separate software programs,procedures, or modules, and thus various subsets of these programs ormodules may be combined or otherwise re-arranged in variousimplementations. In some implementations, memory 314 stores a subset ofthe modules and data structures identified above. Furthermore, memory314 may store additional modules and data structures not describedabove.

Although FIGS. 2 and 3 show client and server computers, these figuresare intended more as functional descriptions of the various featuresthat may be present in these computers than as structural schematics ofthe implementations described herein. In practice, and as recognized bythose of ordinary skill in the art, items shown separately could becombined and some items could be separated. For example, some itemsshown separately in FIGS. 2 and 3 could be implemented in single modulesor data structures.

FIG. 4 illustrates a process of adjusting selection criteria 342-4 forone user based on feedback for content items from one or more otherusers. In this illustration, the content item server 106 has providedthe same content item 324-1 to each of three devices 102-1, 102-2, and102-3 used use user 112-1, 112-2, and 112-3. One or more of these users112 provides some form of feedback to the content item server 106. Thefeedback can be in various forms. In some cases, the feedback is anexplicit positive or negative preference for the specific item 324-1. Insome case, the feedback is a “skip-forward” action, which means the userdid not want to listen or watch the entire content item 324-1. Thefeedback from these users adjusts the selection criteria not only forthemselves, but also the selection criteria 342-4 for User 4 112-4. Theadjustment depends not only on the feedback, but other factors as well,including: any known correlation (or inverse correlation) between thelikes of User 4 112-4 and the likes of the other users; any correlationbetween the content item 324-1 and any other content items; theconsistency of the feedback from the users; and so on. For example, auser may search for and listen to song A, then follow up by searchingfor and listening to song B. Even if songs A and B are completelydifferent, the temporal connection by a single user indicates some sortof correlation, which may apply to other users as well. When User 4112-4 requests a content item (either an individual content item 324 ora sequence of content items 324), the content item server 106 uses theadjusted criteria 342-4 to select the next content item for delivery toDevice 4 102-4. The delivered content item may be the same content item324-1 (or similar item) that was provided to the other users (e.g., iftheir feedback was positive), or the delivered content item may besomething else (e.g., if the feedback was negative).

FIG. 5 illustrates a process of identifying attributes 334 of contentitems 324 based on playlists 338. In this illustration, one user hascreated a playlist called “My Classic Rock” 338-1, which includes thetitle “Rocket Man” 328-1. In some implementations, “Classic Rock” is acontent item attribute 334-1, and thus the title of the user's playlistsuggests that “Rocket Man” should be classified (502) with the “ClassicRock” 334-1 attribute. Although the opinion of one user may not besignificant, if there are a sufficient number of users who make thatcorrelation, the content item selection module 322 can conclude theclassification is correct.

Sometimes a playlist has a non-descript title like “List 1” 338-2.Unlike the title “My Classic Rock” 338-1, the title “List 1” 338-2 doesnot directly provide any useful information. On the other hand, theremay be postings to online social media 114 that help associate thecontent items 324 in a playlist (such as content item 328-2 in playlist338-2) with relevant attributes. For example, a posting to Twitter® orFacebook® may identify a characteristic of a playlist and provide a linkto the list. In social media posting 506, a user refers to his “playlistfor class rock” and provides a link to his list. In this case, thecombination of the social media posting 506 with the playlist 338-2provides the inference (504) that the content item “Dark Side of theMoon” 328-2 should be classified as “Classic Rock.” Just like withplaylist titles, a correlation by a single user may not be significant,but correlation by enough users increases the likelihood that many otherpeople would agree with the classification. Of course both methods (502and 504) of correlating a content item with an attribute can be usedtogether to get even greater significance. In some instances, thecontent item selection module 322 can correlate an attribute with acontent item using just playlist titles, but social media postings canincrease the certainty of the correlation.

Once a content item is associated with an attribute, the attribute canbe used by other users to build playlists 338 or Internet radiostations.

FIGS. 6A and 6B provide a flowchart for a method 600 of selecting (602)content items 324 for users 112. The method is performed (604) by acontent item selection module 322 at an electronic device (e.g., contentserver 106) having one or more processors and memory storing one or moreprograms for execution by the one or more processors. In someimplementations, at least part of the method is performed at a clientdevice 102.

The content item selection module 322 provides (606) a first contentitem 324-1 to a first user 112-1. In some implementations, proving thefirst content item 324-1 to the first user 112-1 is in response to asearch for content items performed by the first user 112-1. The firstcontent item 324-1 is selected (608) from a plurality of availablecontent items 324. In some implementations, the first content item 324-1is (610) an audio track. In other implementations, the first contentitem 324-1 is (612) a video. In some implementations, the content itemsare still images or sequences of still images, animations, interactivegames, or 3-D environments. In some implementations, the first contentitem 324-1 is provided (614) to a first plurality of users 112, inaddition to the first user 112-1. In some implementations, a sequence ofcontent items 324 is provided (616) to the first user 112-1, where thesequence includes the first content item 324-1.

In some cases, the user 112-1 provides some form of feedback about thefirst content item 324-1. The content item selection module 322 receives(618) the input relating to the first content item 324-1 from the firstuser 112-1. In some cases, the input relating to the first content item324-1 is (620) a skip forward input. A user uses skip forward to jump tothe end of a content item (e.g., audio track or video), typically whenthe content item is not desired (at least not at the current time). Insome cases, the input relating to the first content item 324-1 is theabsence of a skip forward input, which is an implicit positiverecognition of the content item 324-1. In some cases, the user interface206 on the client device 102 provides one or more feedback controls,when enables the first user 112-1 to indicate (622) a negativepreference for the first content item 324-1 or indicate (624) a positivepreference for the first content item 324-1. Typically, an explicitpositive preference has a higher significance than an implicitpreference (e.g., lack of skip forward). When the first content item isprovided to a plurality of first users, the content item selectionmodule 322 may receive (626) respective inputs relating to the firstcontent item from two or more respective users of the first plurality ofusers 112. Like the first user 112-1, the two or more respective usersmay provide feedback in the form of skip forward inputs, indications ofpositive preference, or indications of negative preference. When asequence of content items 324 is provided, the feedback can be thelength of time spent with the sequence (e.g., listening or watching), orthe number of times a user 112 returns to the sequence (e.g., returningto an Internet radio station).

The content item selection module 322 then adjusts the selectioncriteria 342-1 for the user 112-1 who provided the feedback. Inaddition, the content item selection module 322 adjusts (628) the itemselection criteria 342-4 for a second user 112-4 distinct from the firstuser. As illustrated in FIG. 4 above, adjusting the content itemselection criteria 324-4 for the second user is (630) sometimes based oninputs received from two or more users. Typically, adjusting the contentitem selection criteria 342-4 is (632) based on a plurality of metrics.In some implementations, the metrics include (634) measuring the numberof skip forward inputs received from the first user. In someimplementations, the metrics include (636) measuring the number ofinputs received from the first user indicating a negative preference forrespective content items in the sequence. In some implementations, themetrics include (638) measuring the number of inputs received from thefirst user indicating a positive preference for respective content itemsin the sequence. In some implementations, the metrics include (640)measuring the amount of time that the sequence of content items wasprovided to the first user. In some implementations, the metrics include(642) measuring the number of times that the first user initiated thesequence of content items. Typically, initiating a sequence of contentitems does not require starting at the same point, or even that thesequence occurs in exactly the same order. For example, this wouldinclude returning to the same Internet radio station. The content itemselection module 322 can use a single one of these identified metrics,or a plurality, and may include additional metrics.

After the item selection criteria 342-4 for the second user have beenadjusted, the second user requests a content item 324 (or sequence ofcontent items 324). The content item selection module 322 receives (644)the request for a content item 324 from the second user 112-4. Inresponse to the request, the content item selection module 322 selects(646) a content item from the plurality of available content items 324for the second user 112-4 according to the adjusted content itemselection criteria 342-4. The content server 106 then provides theselected content item to the second user 112-4. As described above withrespect to FIG. 4, the content item selected for the second user 112-4is not necessarily the same as the first content item 324-1 that wasprovided to the first user. Indeed, negative feedback from other userscould reduce the likelihood of providing the first content item 324-1 tothe second user 112-4.

FIGS. 7A and 7B provide a flowchart for a method 700 of classifying(702) content items 324. The method is performed (704) by a content itemselection module 322 at an electronic device (e.g., content server 106)having one or more processors and memory storing one or more programsfor execution by the one or more processors. In some implementations, atleast part of the method is performed at a client device 102.

The content server 106 identifies (706) a content item 324-3 that isincludes in respective playlists 338 for a plurality of respectivedistinct users 112. The content server 106 receives (708) respectiveuser-generated information corresponding to the content item 324-3 fromeach of the respective distinct users. The respective user-generatedinformation is (710) either a respective playlist title or the text of asocial network posting that identifies a respective playlist. This wasdescribed in more detail with respect to FIG. 5. In some instances, allof the respective user-generated information consists of (712)respective playlist titles. The respective user-generated informationfrom each respective user 112 specifies (714) a first content itemattribute 334-3 that characterizes the content item 324-3. In someinstances, the first content item attribute 334-3 specifies (716) amusic genre. In other instances, the first content item attribute 334-3specifies (718) a holiday or event, such as Christmas or Hanukkah. Insome instances, at least a plurality of the playlist titles specify(720) a first content item attribute 334-3 by including the firstcontent item attribute 334-3 in the text of the playlist titles (e.g.,playlist 338-1 in FIG. 5). Based on the content item 324-3 in theplaylists 338, and the correlation of the playlists with the firstcontent item attribute 334-3 from the user-generated information, thecontent server assigns (722) the first content item attribute 334-3 tothe first content item 324-3.

Subsequently, the content item selection module 322 receives (724) arequest from a first user 112 for a content tem having the first contentitem attribute. For example, in the context of FIG. 5, a user might wantaudio tracks that are “Classic Rock” 334-1. In response, the contentitem selection module 322 selects (726) a content item 324 according tothe first content item attribute 334-1. For example, in the context ofFIG. 5, the content item selection module 322 may select either “RocketMan” 328-1 or “Dark Side of the Moon” 328-2. The content server 106 thendelivers the content item 324 to the first user.

In some implementations, a user can request a sequence of content items,such as an Internet radio station. In such an implementation, thecontent item selection module 322 receives (730) the request from afirst user for a sequence of content items having the first content itemattribute 334-1. In response to such a request, the content itemselection module 322 selects (732) a plurality of content itemsaccording to the first content item attribute 334-1. In someimplementations, each of the plurality of content items selected by thecontent item selection module 322 was assigned (734) the first contentitem attribute 334-1 based on inclusion in a respective plurality ofplaylists 338. Some playlists have (736) a playlist title that specifiesthe first content item attribute 334-1. Some playlists have (738) acorresponding social network posting that refers to the playlist 338 andhas text that specifies the first content item attribute 334-1. Thecontent server 106 then delivers (740) at least a plurality of theselected content items to the first user. In some implementations,delivery of the selected content items associated with the first contentitem attribute are (742) in conjunction with an Internet radio station.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theimplementations were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious implementations with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method of classifying content items, performedat a computer system having one or more processors and memory storingone or more programs for execution by the one or more processors, themethod comprising: identifying a content item that is included inrespective playlists from a plurality of respective distinct users;receiving respective user-generated information corresponding to thecontent item from each of the respective distinct users, wherein therespective user-generated information is selected from the groupconsisting of a respective playlist title and a text of a social networkposting that identifies a respective playlist, and wherein therespective user-generated information from each respective userspecifies a first content item attribute that characterizes the contentitem; in accordance with the identifying and receiving, assigning thefirst content item attribute to the content item; receiving a requestfrom a first user for a content item having the first content itemattribute; selecting the content item according to the first contentitem attribute; and delivering the content item to the first user. 2.The method of claim 1, further comprising: receiving a request from afirst user for a sequence of content items having the first content itemattribute; selecting a plurality of content items according to the firstcontent item attribute, wherein each of the plurality of content itemswas assigned the first content item attribute based on inclusion in arespective plurality of playlists, wherein each playlist has either (a)a playlist title that specifies the first content item attribute; or (b)a corresponding social network posting that refers to the playlist andhas text that specifies the first content item attribute; and deliveringat least a plurality of the selected content items to the first user. 3.The method of claim 2, wherein delivery of the selected content itemsassociated with the first content item attribute are in conjunction withan Internet radio station.
 4. The method of claim 1, wherein the firstcontent item attribute specifies a music genre.
 5. The method of claim1, wherein the first content item attribute specifies a holiday orevent.
 6. The method of claim 1, wherein all of the respectiveuser-generated information consists of respective playlist titles. 7.The method of claim 1, wherein at least a plurality of the playlisttitles specify a first content item attribute by including the firstcontent item attribute in the text of the playlist titles.
 8. A computersystem for selecting content items, comprising: one or more processors;memory; and one or more programs stored in the memory and configured forexecution by the one or more processors, the one or more programscomprising instructions for: identifying a content item that is includedin respective playlists from a plurality of respective distinct users;receiving respective user-generated information corresponding to thecontent item from each of the respective distinct users, wherein therespective user-generated information is selected from the groupconsisting of a respective playlist title and a text of a social networkposting that identifies a respective playlist, and wherein therespective user-generated information from each respective userspecifies a first content item attribute that characterizes the contentitem; assigning the first content item attribute to the content item inaccordance with the identifying and receiving; receiving a request froma first user for a content item having the first content item attribute;selecting the content item according to the first content itemattribute; and delivering the content item to the first user.
 9. Thecomputer system of claim 8, wherein the one or more programs furthercomprise instructions for: receiving a request from a first user for asequence of content items having the first content item attribute;selecting a plurality of content items according to the first contentitem attribute, wherein each of the plurality of content items wasassigned the first content item attribute based on inclusion in arespective plurality of playlists, wherein each playlist has either (a)a playlist title that specifies the first content item attribute; or (b)a corresponding social network posting that refers to the playlist andhas text that specifies the first content item attribute; and deliveringat least a plurality of the selected content items to the first user.10. The computer system of claim 9, wherein delivery of the selectedcontent items associated with the first content item attribute are inconjunction with an Internet radio station.
 11. The computer system ofclaim 8, wherein the first content item attribute specifies a musicgenre.
 12. The computer system of claim 8, wherein the first contentitem attribute specifies a holiday or event.
 13. The computer system ofclaim 8, wherein all of the respective user-generated informationconsists of respective playlist titles.
 14. The computer system of claim8, wherein at least a plurality of the playlist titles specify a firstcontent item attribute by including the first content item attribute inthe text of the playlist titles.
 15. A non-transitory computer readablestorage medium storing one or more programs configured for execution byone or more processors of a computer system to select content items, theone or more programs comprising instructions for: identifying a contentitem that is included in respective playlists from a plurality ofrespective distinct users; receiving respective user-generatedinformation corresponding to the content item from each of therespective distinct users, wherein the respective user-generatedinformation is selected from the group consisting of a respectiveplaylist title and a text of a social network posting that identifies arespective playlist, and wherein the respective user-generatedinformation from each respective user specifies a first content itemattribute that characterizes the content item; assigning the firstcontent item attribute to the content item in accordance with theidentifying and receiving; receiving a request from a first user for acontent item having the first content item attribute; selecting thecontent item according to the first content item attribute; anddelivering the content item to the first user.
 16. The non-transitorycomputer readable storage medium of claim 15, wherein the one or moreprograms further comprise instructions for: receiving a request from afirst user for a sequence of content items having the first content itemattribute; selecting a plurality of content items according to the firstcontent item attribute, wherein each of the plurality of content itemswas assigned the first content item attribute based on inclusion in arespective plurality of playlists, wherein each playlist has either (a)a playlist title that specifies the first content item attribute; or (b)a corresponding social network posting that refers to the playlist andhas text that specifies the first content item attribute; and deliveringat least a plurality of the selected content items to the first user.17. The non-transitory computer readable storage medium of claim 16,wherein delivery of the selected content items associated with the firstcontent item attribute are in conjunction with an Internet radiostation.
 18. The non-transitory computer readable storage medium ofclaim 15, wherein the first content item attribute specifies a musicgenre.
 19. The non-transitory computer readable storage medium of claim15, wherein the first content item attribute specifies a holiday orevent.
 20. The non-transitory computer readable storage medium of claim15, wherein all of the respective user-generated information consists ofrespective playlist titles.
 21. The non-transitory computer readablestorage medium of claim 15, wherein at least a plurality of the playlisttitles specify a first content item attribute by including the firstcontent item attribute in the text of the playlist titles.